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Here's a link to the post discussing this topic - https://exafunction.com/blog/are-gpus-worth-it
At Exafunction, we've noticed a lot of companies CPUs for machine learning inference workloads. We wrote this post to add some color on this and explain why generally GPUs, if managed properly, are the right hardware for these workloads.
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Hey everyone, today I published 1st video regarding "How you can perform neural Architecture Search without training". Do checkout the video. https://www.youtube.com/watch?v=5Iw9pPdXPzI
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NVIDIA is collaborating with the United Nations Economic Commission for Africa (UNECA) to equip governments and developer communities in 10 nations with data science training and technology to support more informed policymaking and accelerate how resources are allocated. The initiative will empower the countries’ national statistical offices — agencies that handle population censuses data, economic Read article >
The post UN Economic Commission for Africa Engages NVIDIA to Boost Data Science in 10 Nations appeared first on NVIDIA Blog.
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In the NVIDIA Studio celebrates the Open Broadcaster Software (OBS) Studio’s 10th anniversary and its 28.0 software release. Plus, popular streamer WATCHHOLLIE shares how she uses OBS and a GeForce RTX 3080 GPU in a single-PC setup to elevate her livestreams.
The post OBS Studio to Release Software Update 28.0 With NVIDIA Broadcast Features ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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Data is the fuel that makes artificial intelligence run. Training machine learning and AI systems requires data. And the quality of datasets has a big impact on the systems’ results. But compiling quality real-world data for AI and ML can be difficult and expensive. That’s where synthetic data comes in. The guest for this week’s Read article >
The post Rendered.ai Founder and CEO Nathan Kundtz on Using AI to Build Better AI appeared first on NVIDIA Blog.
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Happy to share that our study on designing DNA sequences to control gene expression using generative deep learning is just out!
https://www.nature.com/articles/s41467-022-32818-8
This is a continuation of our previous work, where we learned to 'read' regulatory DNA using deep neural nets, accurately predicting gene expression levels in multiple organisms and finding predictive regulatory grammar across whole gene regulatory regions.
https://www.nature.com/articles/s41467-020-19921-4
Here we combine the predictive models with advanced generative models in an architecture termed ExpressionGAN that can be used to 'write' (design) de novo regulatory DNA with target gene expression levels.
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The PyTorch team recently released a Deep Learning Compiler for NVIDIA GPUs called nvFuser. This compiler automatically creates quick, adaptable kernels, speeding up user networks. Creating quick bespoke “fusion” kernels at runtime also significantly accelerates deep learning networks running on Volta and later CUDA accelerators. The new and updated compiler, nvFuser, supports a variety of network architectures as well as applications with dynamic inputs of different shapes and strides and has been specially created to address the particular needs of the PyTorch community. In order to optimize and accelerate PyTorch operations, nvFuser uses graphical representations. Users’ PyTorch operations are not directly accessible as a complete program that a system like nvFuser can optimize because PyTorch uses an eager execution approach. As a result, there is a need for intermediary systems that can translate user programs into a format that nvFuser can optimize. These more advanced methods send the captured operations to nvFuser, which can subsequently tailor the user’s script execution for NVIDIA GPUs.
Continue reading |Github link | Reference article
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Reinventing enterprise computing for the modern era, VMware CEO Raghu Raghuram Tuesday announced the availability of the VMware vSphere 8 enterprise workload platform running on NVIDIA DPUs, or data processing units, an initiative formerly known as Project Monterey. Placing the announcement in context, Raghuram and NVIDIA founder and CEO Jensen Huang discussed how running VMware Read article >
The post NVIDIA and VMware CEOs Discuss New Era of Enterprise Computing appeared first on NVIDIA Blog.
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With personalized content more likely to drive customer engagement, businesses continuously seek to provide tailored content based on their customer’s profile and behavior. Recommendation systems in particular seek to predict the preference an end-user would give to an item. Some common use cases include product recommendations on online retail stores, personalizing newsletters, generating music playlist […]
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On its own, a new machine-learning model discovers linguistic rules that often match up with those created by human experts.
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https://github.com/JoaoLages/diffusers-interpret
Looking for contributors to improve this package!
Generated image for the phrase \"A cute corgi with the Eiffel Tower in the background\"
Word importances for the selected region
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https://www.serkansulun.com/midi/
Here is my recent research project. The website has music samples, and links to the paper and the code. It allows conditioning on an arbitrary emotions, using valence-arousal values, and generates 5-instrument (strings, guitar, bass, piano, drums) rock and pop songs. I hope you find it useful.
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Vanessa Rosa’s art transcends time: it merges traditional and contemporary techniques, gives new life to ancient tales and imagines possible futures.
The post Meet the Omnivore: Artist Fires Up NVIDIA Omniverse to Glaze Animated Ceramics appeared first on NVIDIA Blog.
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On August 9, 2022, we announced the general availability of cross-account sharing of Amazon SageMaker Pipelines entities. You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. Customers are increasingly adopting multi-account architectures for deploying and managing machine […]
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Data preparation is the process of collecting, cleaning, and transforming raw data to make it suitable for insight extraction through machine learning (ML) and analytics. Data preparation is crucial for ML and analytics pipelines. Your model and insights will only be as reliable as the data you use for training them. Flawed data will produce […]
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The Metaverse is seen as the fourth wave of computing and networking - the first three being the mainframe, personal computing, and mobile + cloud. Yet, In contrast to the previous waves, the Metaverse adds 3D experience, or immersion.
The post A Comprehensive Definition of the Metaverse appeared first on Data Science Central.
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https://www.youtube.com/watch?v=KJOZEwJ3w0o&ab_channel=JohannezzMusic
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Reptile was developed by open ai and it'll open yo eye🗿
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https://deumbra.com/2022/08/rllib-for-deep-hierarchical-multiagent-reinforcement-learning/
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According to Forbes, data scientists spend about 80% of their time on data collection, cleansing, and preparation, while only 20% of it is left for actual data analysis. Organizations that don’t utilize master data management systems or data warehouses to keep their data clean and accurate end up basing crucial business decisions on bad data. … Read More »Data cleansing for reliable analytics and business intelligence
The post Data cleansing for reliable analytics and business intelligence appeared first on Data Science Central.
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]
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Organizations in the lending and mortgage industry process thousands of documents on a daily basis. From a new mortgage application to mortgage refinance, these business processes involve hundreds of documents per application. There is limited automation available today to process and extract information from all the documents, especially due to varying formats and layouts. Due […]
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https://preview.redd.it/8vrzhm8l4xj91.jpg?width=640&format=pjpg&auto=webp&s=9a5e7fb4694981d9f9184a9fdc099c96273c60a4
https://preview.redd.it/vyrc59624xj91.png?width=1350&format=png&auto=webp&s=b25f1b106ad5c97ed62e8ed8088b3e3a43991480
colab for img2img: https://colab.research.google.com/drive/1NfgqublyT_MWtR5CsmrgmdnkWiijF3P3?usp=sharing
colab for inpainting: https://colab.research.google.com/drive/1whhIiXxjQjbBuiq4lqwh-AlLIjh3l1OB
demo built with gradio: https://github.com/gradio-app/gradio
hosted web demo for stable diffusion: https://huggingface.co/spaces/stabilityai/stable-diffusion
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The latest solution from the Colossal-AI team (https://github.com/hpcaitech/ColossalAI) and BioMap for protein monomer and multimer structure prediction, xTrimo Multimer, has recently become open-source to the public. This new solution can predict both monomer and multimer structure simultaneously accelerating the process by up to 11 times!
https://preview.redd.it/xeg7wb79btj91.png?width=852&format=png&auto=webp&s=5a9308c9e87691e8a6082d9d3926f10a87e272a6
The hero behind is Colossal-AI, which is a powerful deep learning system that aims to make large AI model training easy and accessible in the community and industry. By integrating large model training techniques and optimizations provided by Colossal-AI, we can significantly reduce the time and cost of both protein monomer and multimer …
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Machine learning (ML) model deployments can have very demanding performance and latency requirements for businesses today. Use cases such as fraud detection and ad placement are examples where milliseconds matter and are critical to business success. Strict service level agreements (SLAs) need to be met, and a typical request may require multiple steps such as […]
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In the digital world, providing information in a local language isn’t novel, but it can be a tedious and expensive task. Advancements in machine learning (ML) and natural language processing (NLP) have made this task much easier and less expensive. We have seen increased adoption of ML for multi-lingual data and document processing workloads. Enterprise […]
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Looking for a change of art? Try using AI — that’s what 3D artist Nikola Damjanov is doing. Based in Serbia, Damjanov has over 15 years of experience in the graphics industry, from making 3D models and animations to creating high-quality visual effects for music videos and movies. Now an artist at game developer company Read article >
The post 3D Artist Creates Blooming, Generative Sculptures With NVIDIA RTX and AI appeared first on NVIDIA Blog.
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E-commerce sales have skyrocketed as more people shop remotely, spurred by the pandemic. But this surge has also led fraudsters to use the opportunity to scam retailers and customers, according to David Sutton, director of analytical technology at fintech company Featurespace. The company, headquartered in the U.K., has developed AI-powered technology to increase the speed Read article >
The post Fintech Company Blocks Fraud Attacks for Financial Institutions With AI and NVIDIA GPUs appeared first on NVIDIA Blog.
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Some weeks, GFN Thursday reveals new or unique features. Other weeks, it’s a cool reward. And every week, it offers its members new games. This week, it’s all of the above. First, Saints Row marches into GeForce NOW. Be your own boss in the new reboot of the classic open-world criminal adventure series, now available Read article >
The post GFN Thursday Adds ‘Saints Row,’ ‘Genshin Impact’ on Mobile With Touch Controls appeared first on NVIDIA Blog.
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What would it take to get humanoid, bipedal robots to dance like Mick Jagger? Indeed, for something more mundane, what does it take to get them to simply stand still? Sit down? Walk? Move in myriads of other ways many people take for granted? Bipedalism provides unparalleled versatility in an environment designed for and by […]
The post MoCapAct: Training humanoid robots to “Move Like Jagger” appeared first on Microsoft Research.
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What is neural network ?
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Hello all!
This weekend I wrote: goa-loader, National Gallery of Art Open Data Program tf.data.Dataset Loader and generative modeling to accompany
Let me know if you use the loader. Open to PRs to add GANs or anything else interesting, enjoy!
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https://preview.redd.it/kzr84j69dnj91.png?width=2228&format=png&auto=webp&s=df30cd90fd3486f83e5630eaee782eaac1e0a197
More info at https://outsystems-ai-reading-group.github.io/
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https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ mentions that neural networks learn a representation of the data so as to make the classes linearly separable. What I fail to see is how does a neural network create a separating hyperplane that separates the classes. How do we know that it creates a hyperplane? What's the math behind it?
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Lincoln Laboratory Supercomputing Center dataset aims to accelerate AI research into managing and optimizing high-performance computing systems.
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Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
Introduction
Our
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In the AWS Deep Learning Challenge held from January 5, 2022, to March 1, 2022, participants from academia, startups, and enterprise organizations joined to test their skills and train a deep learning model of their choice using Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances and Habana’s SynapseAI SDK. The EC2 DL1 instances powered by […]
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A year ago we presented a new position embedding method. It's now used in BigScience's BLOOM model and in a few other models.
I just uploaded a video lecture where I explain this ALiBi method and also talk about lots of other topics related to training large language models
https://www.youtube.com/watch?v=Pp61ShI9VGc
I discuss why transformers overfit to the commonly used absolute (learned/sinusoidal) position embeddings and I discuss paths for future work in long-sequence modeling.
Feel free to ask any questions here :)
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Upscaled in Photoshop Beta
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Hey everyone! The Learn AI Together community is getting bigger and bigger with more and more people learning AI, we would love to find more enthusiastic professionals (junior,mid-level, senior..), and grad students, TAs or professors willing to help and exchange with people learning AI by answering questions from time to time.
We are an AI-enthusiasts community of over 28'000 people now where members can chat, ask questions, share resources and projects, find people to work with, find job offers, etc. We are now focusing on getting experts or advanced members to join us and help us help others.
More info about the community and how to join us (free): Learn AI Together
Excited to chat with you there!
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A graph with five billion people (nearly everyone on the Internet) brings up both ethical questions and opportunities.
The post DSC Weekly 23 August 2022: Five Billion Person Graph – Grand Achievement or Wakeup Call? appeared first on Data Science Central.
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Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI), an international unaffiliated not-for-profit organization, develops AI-based Data Coding standards with associated clear licensing frameworks.
The post The MPAI-AIF V2 Call for Technologies appeared first on Data Science Central.
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Now with Amazon Forecast, you can seamlessly conduct what-if analyses up to 80% faster to analyze and quantify the potential impact of business levers on your demand forecasts. Forecast is a service that uses machine learning (ML) to generate accurate demand forecasts, without requiring any ML experience. Simulating scenarios through what-if analyses is a powerful […]
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AI Centers of Excellence are organizational units dedicated to implementing a company-wide AI vision. They help identify business use cases, create an implementation roadmap, accelerate adoption, assess impact and more. NVIDIA GTC, a global conference on AI and the metaverse, brings together the world’s top business and technology leaders who’ve embraced artificial intelligence to transform Read article >
The post Learn How Leading Companies Are Building AI Centers of Excellence, at NVIDIA GTC appeared first on NVIDIA Blog.
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Floods in Kentucky and wildfires in California are the kinds of disasters companies of all sorts are trying to address with AI. Tom Rikert, co-founder and CEO of San Francisco-based startup Masterful AI, is one of many experts helping them manage catastrophe risk. In the U.S. alone, the National Association of Insurance Commissioners estimates that Read article >
The post Shelter From the Storm: AI Helps Gauge Catastrophe Risks appeared first on NVIDIA Blog.
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Recent AI advances enable modeling of weather forecasting 4-5 magnitudes faster than traditional computing methods. The brightest leaders, researchers and developers in climate science, high performance computing and AI will discuss such technology breakthroughs — and how they can help foster a greener Earth — at NVIDIA GTC. The virtual conference, running Sept. 19-22, also Read article >
The post Predict, Detect, Mitigate: AI for Climate Science Takes the Stage at NVIDIA GTC appeared first on NVIDIA Blog.
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A triple threat steps In the NVIDIA Studio this week: a tantalizing trio of talented 3D artists who each reimagined and remastered classic European buildings with individualistic flair.
The post 3D Artists Reimagine, Remaster Iconic European Architecture This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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Find impressive examples of ML that we use every day.
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In case you haven't noticed, stability.ai just open-sourced their latest version of StableDiffusion to the public. Here is the link: https://stability.ai/blog/stable-diffusion-public-release
It is so fast and small (memory footprint) that it can run on consumer grade GPUs. I just generated my first "astronaut riding a horse on mars" on my local GTX3090.
Astronaut riding a horse on mars
So what is opinion on open-sourcing such powerful models ? And, what do you think about stability.ai as an organisation ? Do you feel they can potentially be the next OpenAI ?
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10 years ago I had the idea to write a children’s book about the joy and difficulties of building something; That idea was there but the hassle and cost of illustration always prevented me from finishing the book.
Recently, Dall-E was launched and I excitedly used it for my children's book illustrations. You can see the result and also issues I faced such as maintaining a consistent art style and face removal.
https://medium.com/@rwanghacker/creating-and-illustrating-a-childrens-book-with-dall-e-in-less-than-a-week-813ee85f2225
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Due to domain issues, the up-to-date documentation for Gym is now hosted at https://gymlibrary.dev
The documentation is maintained by the Farama Foundation on GitHub, and contributions are always welcome!
The best way to get in touch with the team is on the Discord server
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Blockchain appears to be establishing the foundation of a new economic system, though significant challenges remain.
The post Blockchain Technology: The Potential to Change the Future appeared first on Data Science Central.
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Knowledge management is refactoring the way that organizations work.
The post How Knowledge Management Can Reshape Business Operations appeared first on Data Science Central.
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While Knowledge Graph hype is nowhere near as loud as AI hype, there is no question that more and more organizations are turning to knowledge graphs to solve real-world problems.
The post From Knowledge Graphs To Knowledge Portals appeared first on Data Science Central.
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Amazon Kendra is an intelligent search service powered by machine learning (ML). With Amazon Kendra, you can easily aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. Many organizations use the content management platform Alfresco to store […]
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An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
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Workspace security can be a fiddly money drain, especially for corporations that deal with sensitive information, or run multiple offices…
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Watching out for the nearly-extinct two-ton beasts may be the ultimate example of a job best done remotely.
The post An AI-Enabled Drone Could Soon Become Every Rhino Poacher’s… Horn Enemy appeared first on NVIDIA Blog.
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Sipeed TinyMaix is an open-source machine learning library designed for microcontrollers. According to findings, it is lightweight enough to be compatible with Microchip ATmega328 MCU found in the Arduino UNO board and its many clones.
The core code of TinyMax, which was created during a weekend hackathon, has roughly 400 lines, a binary size of about 3KB, and uses very little RAM, allowing it to execute the MNIST handwritten digit classification on an ATmega320 MCU with only 2KB SRAM and 32KB flash.
Continue reading | Github
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I wrote a post on how to virtualise GPUs and attach them to VMs for on-prem workloads. The VMs can then be attached to Juju & K8S for load balancing or whatever you want. I implemented this where I work and it runs all of our ML compute, it was a pain to get working originally hope that you find it useful:
https://www.paulcjh.com/technical_posts/gpu_virtualisation.html
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What’s NNext?
[Say “Next”]
NNext is a blazingly fast ⚡️, open-source 📖, (vector) neural search 🔎 engine for building delightful AI Apps 🦾.
Github: https://github.com/nnextdb/nnext
Applications
Vector search is a key concept in modern machine learning systems. It’s the technology behind
🎖 Recommendation systems (such as Instagram’s “Explore” page).
🔎 Search system of all kinds
Text search. For instance google’s “related search”
Image Search. Such as reverse image search.
🤖 Chatbots and question answering systems
🧼 Data cleaning and pre-processing. Used to de-duplicate data representing similar items.
🏹 One-shot/zero-shot learning
🧌 Fraud and outlier detection
Problem
Existing vector search packages are usually in the form of open-source packages released by la…
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Implementation & LM Eval Harness Results
LLM.int8() Paper
LLM.int8() r/MachineLearning discussion
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In four talks over two days, senior NVIDIA engineers will describe innovations in accelerated computing for modern data centers and systems at the edge of the network. Speaking at a virtual Hot Chips event, an annual gathering of processor and system architects, they’ll disclose performance numbers and other technical details for NVIDIA’s first server CPU, Read article >
The post NVIDIA to Share New Details on Grace CPU, Hopper GPU, NVLink Switch, Jetson Orin Module at Hot Chips appeared first on NVIDIA Blog.
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Imagine taking a selfie and using it to get a moving, talking, customizable 3D avatar of yourself in just seconds.
The post Meet the Omnivore: Startup in3D Turns Selfies Into Talking, Dancing Avatars With NVIDIA Omniverse appeared first on NVIDIA Blog.
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In the last 10 years, healthcare has been one of the fastest-growing sectors of the economy, i.e., the global economy as a whole. As…
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So much data, so little time. Machine learning (ML) experts, data scientists, engineers and enthusiasts have encountered this problem the world over. From natural language processing to computer vision, tabular to time series, and everything in-between, the age-old problem of optimizing for speed when running data against as many GPUs as you can get has […]
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One of the challenges encountered by teams using Amazon Lookout for Metrics is quickly and efficiently connecting it to data visualization. The anomalies are presented individually on the Lookout for Metrics console, each with their own graph, making it difficult to view the set as a whole. An automated, integrated solution is needed for deeper […]
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https://deoxyribose.github.io/No-Shortcuts-to-Knowledge/
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Most of the time, when I try to implement (and understand) a new code on GitHub I feel overwhelmed by the complexity of it. In my opinion, one of the hardest part is trying to understand its structure and how it works.
When you are facing this issue, what is your modus operandi? Does it make sense to produce a map of the code structure? Does it make sense to spend a bit of time to create it while digging into it? If you are already doing it, is there any tool that produces an automatic map of the code?
An example can be found here: https://github.com/dvlab-research/ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions/raw/main/figs/model_mind_flow.png
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When Rachel Carpenter and Joseph French founded Intrinio a decade ago, the fintech revolution had only just begun. But they saw an opportunity to apply machine learning to vast amounts of financial filings to create an alternative data provider among the giants. The startup, based in St. Petersburg, Fla., delivers financial data to hedge funds, Read article >
The post Startup Digs Into Public Filings With GPU-Driven Machine Learning to Serve Up Alternative Financial Data Services appeared first on NVIDIA Blog.
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AI and the metaverse are revolutionizing every aspect of the way we live, work and play — including how we move. Leaders in the automotive and technology industries will come together at NVIDIA GTC to discuss the newest breakthroughs driving intelligent vehicles, whether in the real world or in simulation. The virtual conference, which runs Read article >
The post Boldly Go: Discover New Frontiers in AI-Powered Transportation at GTC appeared first on NVIDIA Blog.
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Cameras have been deployed in factories for over a decade — so why, Franz Tschimben wondered, hasn’t automated visual inspection yet become the worldwide standard? This question motivated Tschimben and his colleagues to found Covision Quality, an AI-based visual-inspection software startup that uses NVIDIA technology to transform end-of-line defect detection for the manufacturing industry. “The Read article >
The post Startup’s Vision AI Software Trains Itself — in One Hour — to Detect Manufacturing Defects in Real Time appeared first on NVIDIA Blog.
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Class is in session this GFN Thursday as GeForce NOW makes the up-grade with support for higher resolutions and frame rates in Chrome browser on PC. It’s the easiest way to spice up a boring study session. When the lecture is over, dive into the six games joining the GeForce NOW library this week, where Read article >
The post Easy A: GeForce NOW Brings Higher Resolution and Frame Rates for Browser Streaming on PC appeared first on NVIDIA Blog.
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By 2022, the AI in FinTech market will be worth $7.25 billion. Artificial Intelligence (AI) is driving a new wave in FinTech. From banks to…
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A new lecture from Andrej Karpathy on his YouTube channel: https://www.youtube.com/watch?v=VMj-3S1tku0
This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school.
According to Karpathy, "this is the culmination of about 8 years of obsessing about the best way to explain neural nets and backprop."
He also mentions, "If you know Python, have a vague recollection of taking some derivatives in your high school, watch this video and not understand backpropagation and the core of neural nets by the end then I will eat a shoe :D"
Link to the YouTube video: https://www.youtube.com/watch?v=VMj-3S1tku0
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https://forms.gle/rPKmuN611VeLmZaNA
I'm conducting a survey to understand awareness, attitudes and readiness of high school students towards Artificial Intelligence.
The study will look at different aspects such as opportunities, risks, and ethics of AI, and also education necessary for high schoolers to improve their understanding. The results will be published as part of a detailed report.
Your inputs are valuable in understanding how students learn and think about AI.
All responses will be kept confidential and data is analysed only at the aggregate level.
The “best” five responses will get a Rs 1000 amazon gift card each. The winners will be selected by, you guessed it, an algorithm.
Thank You!
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The AWS website is currently available in 16 languages (12 for the AWS Management Console and for technical documentation): Arabic, Chinese Simplified, Chinese Traditional, English, French, German, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Thai, Turkish, and Vietnamese. Customers all over the world gain hands-on experience with the AWS platform, products, and services in their […]
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We are excited to announce that Amazon Personalize now supports incremental bulk dataset imports; a new option for updating your data and improving the quality of your recommendations. Keeping your datasets current is an important part of maintaining the relevance of your recommendations. Prior to this new feature launch, Amazon Personalize offered two mechanisms for […]
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Mapping the immune system could lead to the creation of drugs that help our bodies win the fight against cancer and other diseases. That’s the big idea behind immunotherapy. The problem: the immune system is incredibly complex. Enter Immunai, a biotech company that’s using cutting-edge genomics & ML technology to map the human immune system Read article >
The post Immunai Co-Founder Luis Voloch on Using Deep Learning to Develop New Drugs appeared first on NVIDIA Blog.
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What are your favourite RSS/Atom feeds you consume to stay up to date with all things AI/ML in terms of News sites, Podcasts, Blogs or even YouTube channels?
Ideally anything that isn't already covered by sites like allainews.com.
Things like:
research, innovations
new libraries & how to use them
products/tech related to AI/ML
educational stuff, how to get into AI/Data Science
anything else that's related
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Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. For example, you can find your logo in social […]
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Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use […]
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We're currently working on a new open-source notebook to shape the future of building data pipelines.
We would love for you to test out our current version in a collaborative effort to create better workflows for data scientists (and other data and machine learning professionals).
Repo: https://github.com/mage-ai/mage-ai
More about Mage: https://mage.ai
Join our slack community: https://mage.ai/chat
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Detectron2 is a very popular PyTorch-based library for detection tasks. Although it has optimal implementations of many detection and segmentation models, it does not provide a good way to deploy the models to production. I share in the post below how I deployed Detectron2 models with Triton inference server (an inference system developed by NVIDIA). Hope you find it's helpful!
https://tintn.github.io/deploy-detectron2-with-triton/
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Hi,
We recently were building a simple model training as a service for GPU jobs and came across run.ai.
There has been mentions of GPU virtualization but not quite sure how to use it with the existing Kubernetes setup of ours.
Are there any organisations using Run AI currently? Are there any opensource alternatives for the same?
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What are your favourite RSS/Atom feeds you consume to stay up to date with all things AI/ML in terms of News sites, Podcasts, Blogs or even YouTube channels?
Ideally anything that isn't already covered by sites like allainews.com.
Things like:
research, innovations
new libraries & how to use them
products/tech related to AI/ML
educational stuff, how to get into AI/Data Science
anything else that's related
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Available at: https://github.com/google-research/dataclass_array
`dataclass_array` allow to have structured data that can be of arbitrary batch shape. For example:
Defining a dataclass array:
@dataclasses.dataclass(frozen=True) class Ray(dca.DataclassArray): pos: FloatArray['*batch_shape 3'] dir: FloatArray['*batch_shape 3']
Dataclass array can then be manipulated as if they were ndarray, while keeping the internal semantic structure
rays = camera.rays() # Returns `Ray` with shape `(h, w)` rays.shape == (h, w) rays.pos.shape == (h, w, 3) # Individual ndarray fields accessible rays = rays.reshape('h w -> w h') # Native einops support rays = rays.flatten() rays = rays[..., :30] rays = rays[rays.norm() > 0] # Masking, filtering rays = rays.as_jax() # Native Jax, TF, NumPy... support
For an example of dataclass_array used in practice, see: https://github.com/google-research/visu3d
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In its initial stages of development, Blockchain Technology is predicted to top $2 billion in three years. We’re living in an era of super-smart intelligent machines, rather, now, our future lies in being more human and less like a machine. This is how the consequences of this inevitable rise of technology are engraved in our… Read More »Blockchain Technology Optimizing Early Entrants in Education
The post Blockchain Technology Optimizing Early Entrants in Education appeared first on Data Science Central.
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In today’s digital world, data privacy and protection are increasingly important. Add in the complexity of remote teams, and you have a whole new ball game. It’s undeniable that remote work is favored by employees and now employers too, with 97% of workers. So it’s essential to implement a data privacy and protection strategy that… Read More »How to Implement a Data Privacy and Protection Strategy for Remote Teams
The post How to Implement a Data Privacy and Protection Strategy for Remote Teams appeared first on Data Science Central.
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In the fast-paced field of making the world’s tech devices, Pegatron Corp. initially harnessed AI to gain an edge. Now, it’s on the cusp of creating digital twins to further streamline its efficiency. Whether or not they’re familiar with the name, most people have probably used smartphones, tablets, Wi-Fi routers or other products that Taiwan-based Read article >
The post Smart Devices, Smart Manufacturing: Pegatron Taps AI, Digital Twins appeared first on NVIDIA Blog.
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Imagine driving a car — one without self-driving capabilities — to a mall, airport or parking garage, and using an app to have the car drive off to park itself. Software company Seoul Robotics is using NVIDIA technology to make this possible — turning non-autonomous cars into self-driving vehicles. Headquartered in Korea, the company’s initial Read article >
The post AI Shows the Way: Seoul Robotics Helps Cars Move, Park on Their Own appeared first on NVIDIA Blog.
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Many artists can edit a video, paint a picture or build a model — but transforming one’s imagination into stunning creations can now involve breakthrough design technologies. Kate Parsons, a digital art professor at Pepperdine University and this week’s featured In the NVIDIA Studio artist, helped bring a music video for How Do I Get to Invincible to life using virtual reality and NVIDIA GeForce RTX GPUs.
The post Digital Art Professor Kate Parsons Inspires Next Generation of Creators This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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* an epistemological approach
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Hi! We have opened up our new labeling AI tool to try for free. Their team pioneered the machine teaching concept and the approach allows you to automatically select and generate labels and create a model for labeling more data at the same time. Try it and let us know what you think.
-Intelus.ai
GeekWire: https://www.geekwire.com/2021/no-code-ai-former-microsoft-and-salesforce-execs-reveal-new-machine-teaching-startup-intelus/
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I wrote a blog post about finetuning GPT-3 on HN to determine whether a technical blog post is good or not, and also engineer prompts to GPT-3 to generate alternate titles which can then be ranked.
The code + demos is available open-source on GitHub, although the finetuned model isn't due to OpenAI rules/inability to share models.
(incidentally the post did well on Hacker News)
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Hi! We have just open-sourced our text annotator which runs directly in your notebook. You can now select spans of text for entity extraction and do your processing & modelling all in the same place. This allows for quick iteration when getting a project started. Here is the repository: https://github.com/dataqa/jupyter-annotate.
We would be very happy to hear any feedback or comments you might have!
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I'm training a VQGAN model on a custom dataset and over time I notice repeating artifacts that don't look like anything in the original images. How to interpret the occurrence of these artifacts, is it some sort of partial degradation of the network which leads to a particular part of the Generator firing more often and the Discriminator doesn't penalize it?
Example: https://imgur.com/cqZIJe6
Model: https://github.com/dome272/VQGAN-pytorch
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Hi r/MachineLearning,
I just published this video going over (and visualizing) this paper from 2020 that I can't stop thinking about. Hope you find it interesting.
https://www.youtube.com/watch?v=WQm7-X4gts4
Now that language models have been trained on massive internet-scale text data, where are future improvements going to come from? Jay goes over the "Experience Grounds Language" paper which describes five "World Scopes" for learning language -- including multimodality (e.g. training on images + text) and beyond.
Contents:
Introduction (0:00)
Experience Grounds Language (1:20)
World Scopes (2:58)
World Scope 1 and 2 (3:33)
World Scope 3 - Multimodality (3:56)
World Scope 4 - Embodiment and Action (7:00)
World Scope 5 - The Social World (9:50)
Reading Excerpts from the paper (10:48)
World scopes encompass each other (16:50)
Interesting thought experiments (19:42)
Conclusion (21:09)
Paper: Experience Grounds Language (2020) https://arxiv.org/abs/2004.10151
Authors: Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
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Today, we are excited to announce Promotions feature in Amazon Personalize that allows you to explicitly recommend specific items to your users based on rules that align with your business goals. For instance, you can have marketing partnerships that require you to promote certain brands, in-house content, or categories that you want to improve the […]
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Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML) workflows. When you launch a solution, various of AWS resources are set up in your account to demonstrate how the business problem can be solved using the pre-built architecture. The solutions […]
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Organizations across industries such as healthcare, finance and lending, legal, retail, and manufacturing often have to deal with a lot of documents in their day-to-day business processes. These documents contain critical information that are key to making decisions on time in order to maintain the highest levels of customer satisfaction, faster customer onboarding, and lower […]
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Extended security posture management keeps your data safe by helping IT teams to strengthen the security posture of an infrastructure.
The post Preventing Data Breaches with Extended Security Posture Management appeared first on Data Science Central.
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In the world of marketing, one of the most important aspects is content. Content can be anything from a blog post to an e-book or even an…
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They can be found here
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Dear fellow redditors.
Feel free to join us online (or F2F in Sydney, Australia) for a 2 days symposium on Creative AI.
We'll having great panel discussions with researchers and artists-creators from Canva, NVidia, UNSW and many others.
Event is free :)
More here: https://www.eventbrite.com.au/e/creative-ai-sydney-tickets-336053002577
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https://www.youtube.com/watch?v=1mTonZh5SGk
Enjoy!
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https://youtu.be/YQ2QtKcK2dA
OUTLINE:
0:00 - Intro
1:30 - What is Stability AI?
3:45 - Where does the money come from?
5:20 - Is this the CERN of AI?
6:15 - Who gets access to the resources?
8:00 - What is Stable Diffusion?
11:40 - What if your model produces bad outputs?
14:20 - Do you employ people?
16:35 - Can you prevent the corruption of profit?
19:50 - How can people find you?
22:45 - Final thoughts, let's destroy PowerPoint
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Data integration is one of the most crucial resources within every business. But it’s certainly not a limited resource. As a matter of fact, data is one of the most expansive and complex resources that’s difficult to process and manage. As a business expands, the amount of exact data it needs to integrate, analyze, and… Read More »How Customer Data Integration Can Take Your Business to the Next Level
The post How Customer Data Integration Can Take Your Business to the Next Level appeared first on Data Science Central.
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For over a decade, NVIDIA has invested in social causes and communities in India as part of our commitment to corporate social responsibility. Bolstering those efforts, we’re unveiling this year’s investments in five projects that have been selected by the NVIDIA Foundation team, focused on the areas of environmental conservation, ecological restoration, social innovation and job Read article >
The post From Sapling to Forest: Five Sustainability and Employment Initiatives We’re Nurturing in India appeared first on NVIDIA Blog.
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AI Weirdness: the strange side of machine learning
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In recent years, businesses have leveraged big data to gain insights for business decision-making. However, managing large amounts of data…
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If you're working with artificial intelligence it's crucial to keep up with the latest trends in this industry. That's why I want to share with you this podcast on the top AI trends. Hope you'll find it helpful.
AI in security and surveillance
AI in real-time video processing
AI for content creation and chatbots
Other NLP solutions
Use of GANs
AI-driven visual inspection for production
AI in healthcare
No-code AI platforms
Diversity in AI
Listen to the podcast https://youtu.be/UVl_WKRyZuI
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In this seminar Professor Hinton will present a single idea about representation which allows advances made by several different groups to be combined into an imaginary system called GLOM. GLOM answers the question, "How can a neural network with a fixed architecture parse an image into a part-whole hierarchy which has a different structure for each image?" Watch on YouTube
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I just want to share a paper I recently published with Peter Singer. We argued that AI ethics should extend its scope to nonhuman animals. We also analyzed whether, and how, AI agents can behave ethically toward animals. Please kindly consider giving us feedback if you read the paper, thank you!
https://link.springer.com/article/10.1007/s43681-022-00187-z
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In an online world swamped with fake news machine learning is playing an increasingly important role in cleaning up our collective information ecology.
At Full Fact we have been developing technology to help increase the speed of fact-checking. Recently we have been working on a tool that can automatically fact-check claims from the UK media without any human input. This tool is capable of extracting the key information from a claim before looking up the relevant data and determining its veracity.
In this video we breakdown how we combined deep learning with more traditional NLP techniques to create this cutting-edge tool.
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Good evening, guys. I currently use StarCraft 2 as a tool for experimenting with my deep reinforcement learning projects, I have also used OpenAI Gym.
The intention of this post is to open my horizon of possibilities and take this content to my research group to see if it helps someone.
Could you recommend other tools for experimentation? There is no search theme restriction.
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Today, air pollution is a familiar environmental issue that creates severe respiratory and heart conditions, which pose serious health threats. Acid rain, depletion of the ozone layer, and global warming are also adverse consequences of air pollution. There is a need for intelligent monitoring and automation in order to prevent severe health issues and in […]
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Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine […]
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Israel’s largest private medical center is working with startups and researchers to bring potentially life-saving AI solutions to real-world healthcare workflows. With more than 1.5 million patients across eight medical centers, Assuta Medical Centers conduct over 100,000 surgeries, 800,000 imaging tests and hundreds of thousands of other health diagnostics and treatments each year. These create Read article >
The post Top Israel Medical Center Partners with AI Startups to Help Detect Brain Bleeds, Other Critical Cases appeared first on NVIDIA Blog.
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It’s time to rumble in Grapital City with Rumbleverse launching today on GeForce NOW. Punch your way into the all-new, free-to-play Brawler Royale from Iron Galaxy Studios and Epic Games Publishing, streaming from the cloud to nearly all devices. That means gamers can tackle, uppercut, body slam and more from any GeForce NOW-compatible device, including Read article >
The post GFN Thursday Brings Thunder to the Cloud With ‘Rumbleverse’ Arriving on GeForce NOW appeared first on NVIDIA Blog.
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Programming Language Evaluation
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Is there a tutorial that can help me learn what this guy did to predict stock prices using pennylane? Anything I try to find ends up just being something about building circuits and other stuff when really I just want to figure out what this guy did and fully understand it
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Hey everyone,
I think there are not many of people like me who would love to apply more AI to audio related topics which are not only text-to-speech. Recently the only youtuber I know about this and really like, Valerio Velardo just launched a new platform.
At the moment there is only the advanced python tutorial but there will be nice things coming there I guess, so if you also search for resources in audio related AI I highly recommend his channel while waiting for what is coming there.
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I'm a backend engineer at ByteDance (Tiktok). We are maintaining a very complex DeepLearning-based Recommendation System. I was always thinking about how to make a 70% performance but easy-to-adopt recommendation system for smaller scenarios.
Maybe most people here write Python, this project is Golang based. As we all know, taking a complex CTR model with a lot of feature engineering work online is really painful.
So, let's suppose you know how to write Golang and SQL. It will be really easy to use edgeRec to train and deploy an API ready CTR prediction system over your database like MySQL or SQLite.
This project is a pretty prototype, any suggestion is welcome.
Right now, I'm just trying to make the AUC better by implementing the Deep Interest Network model.
I will try to make it w…
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I feel as though this area has not received much attention over the last couple of years. The CleverHans project has gone stale and I haven't heard of many new results recently. Has the community lost interest in this area? Did we decide that adversarial attacks aren't such a problem in practical applications?
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Book available here as pdf: https://probml.github.io/pml-book/book2.html
ToC here, if you want to peek: https://github.com/probml/pml2-book/blob/main/toc2-long-2022-07-29.pdf
Discussion, from the man himself: https://twitter.com/sirbayes/status/1553127082992881665
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In the agriculture sector, the problem of identifying and counting the amount of fruit on trees plays an important role in crop estimation. The concept of renting and leasing a tree is becoming popular, where a tree owner leases the tree every year before the harvest based on the estimated fruit yeild. The common practice […]
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Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations. SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify for your […]
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Engineers 3D print materials with networks of sensors directly incorporated.
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The MIT researcher and former professor discusses how Covid-19 and the influx of virtual technologies created a new medical ecosystem that needs more synchronized oversight.
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The faculty members will work together to advance the cross-cutting initiative of the MIT Schwarzman College of Computing.
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Graduate student Jana Saadi works on making the product design process more creative and inclusive.
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We are introducing a new-and-improved content moderation tool: The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.
To help developers protect their applications against possible misuse, we are introducing the faster and more accurate Moderation endpoint. This endpoint provides OpenAI
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Asked about the future of design, Donald Greenberg holds up a model of a human aorta. “After my son became an intravascular heart surgeon at the Cleveland Clinic, he hired one of my students to use CAT scans and create digital 3D models of an aortic aneurysm,” said the computer graphics pioneer in a video Read article >
The post Design in the Age of Digital Twins: A Conversation With Graphics Pioneer Donald Greenberg appeared first on NVIDIA Blog.
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Tokyo-based startup Telexistence this week announced it will deploy NVIDIA AI-powered robots to restock shelves at hundreds of FamilyMart convenience stores in Japan. There are 56,000 convenience stores in Japan — the third-highest density worldwide. Around 16,000 of them are run by FamilyMart. Telexistence aims to save time for these stores by offloading repetitive tasks Read article >
The post AI Flying Off the Shelves: Restocking Robot Rolls Out to Hundreds of Japanese Convenience Stores appeared first on NVIDIA Blog.
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With the rise of artificial intelligence and machine learning, it’s not surprising that content marketing is becoming a key aspect of…
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One of the challenges that decentralized finance (and the web in general) faces is the need to uniquely identify a person, an organization, or a product. This, in general, is difficult because open identifiers are easily spoofed. Blockchain largely intended to combat this by creating self-sovereignty through a distributed algorithm that verified transactions were recorded and captured in multiple places.
The post DSC Weekly 09 August 2022 – Decentralized Identifiers (DiDs) becomes a W3C Recommendation appeared first on Data Science Central.
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Recently, Tim O Reilly posted his vision of the metaverse which resonates with me It’s not exactly an earth shattering revelation – but it’s easy to forget that the Web and also the Metaverse – are primarily social mediums. It’s easy to get caught up in the Web 3/ NFT real estate metaphor and forget… Read More »WFH – Is the metaverse based on shared activity instead of shared space?
The post WFH – Is the metaverse based on shared activity instead of shared space? appeared first on Data Science Central.
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Hi everyone!
We're excited to release EvoTorch: An open-source Evolutionary Algorithm Library with multi-CPU/multi-GPU support for massive evolutionary experiments!
We are researchers and engineers in industrial automation at NNAISENSE and this library is the latest version of the tool we've been using in our own work. It is built on top of PyTorch and Ray, and provides a collection of state-of-the-art evolutionary algorithms, out-of-the-box support for scaling experiments to arbitrary clusters of CPUs and GPUs, advanced tools for NeuroEvolution of any PyTorch module, and direct interfaces to modern logging libraries to track experiments and integrate with existing workflows.
Our goal is to make many more engineers be able to use EAs in their workflow, and help researchers do better research.
We are happy to have your feedback or questions here or on our Slack channel!
EvoTorch is pip installable: pip install evotorch
Read the docs: https://docs.evotorch.ai
The library is completely open source under an Apache 2.0 license: https://github.com/nnaisense/evotorch
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In November 2021, in collaboration with RStudio PBC, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench IDE in the cloud. You can now bring your current RStudio license to easily migrate your self-managed RStudio environments to Amazon SageMaker in just a few simple steps. RStudio is […]
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A glimpse into the future of AI-infused virtual worlds was on display at SIGGRAPH — the world’s largest gathering of computer graphics experts — as NVIDIA founder and CEO Jensen Huang put the finishing touches on the company’s special address.
The post Future of Creativity on Display ‘In the NVIDIA Studio’ During SIGGRAPH Special Address appeared first on NVIDIA Blog.
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In a swift, eye-popping special address at SIGGRAPH, NVIDIA execs described the forces driving the next era in graphics, and the company’s expanding range of tools to accelerate them. “The combination of AI and computer graphics will power the metaverse, the next evolution of the internet,” said Jensen Huang, founder and CEO of NVIDIA, kicking Read article >
The post At SIGGRAPH, NVIDIA CEO Jensen Huang Illuminates Three Forces Sparking Graphics Revolution appeared first on NVIDIA Blog.
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NVIDIA AI tools are enabling deep learning-powered performance capture for creators at every level: visual effects and animation studios, creative professionals — even any enthusiast with a camera. With NVIDIA Vid2Vid Cameo, creators can harness AI to capture their facial movements and expressions from any standard 2D video taken with a professional camera or smartphone. Read article >
The post NVIDIA AI Makes Performance Capture Possible With Any Camera appeared first on NVIDIA Blog.
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For cutting-edge visual effects and virtual production, creative teams and studios benefit from digital sets and environments that can be updated in real time. A crucial element in any virtual production environment is a sky dome, often used to provide realistic lighting for virtual environments and in-camera visual effects. Legendary studio Industrial Light & Magic Read article >
The post As Far as the AI Can See: ILM Uses Omniverse DeepSearch to Create the Perfect Sky appeared first on NVIDIA Blog.
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The creation of 3D objects for building scenes for games, virtual worlds including the metaverse, product design or visual effects is traditionally a meticulous process, where skilled artists balance detail and photorealism against deadlines and budget pressures. It takes a long time to make something that looks and acts as it would in the physical Read article >
The post New NVIDIA Neural Graphics SDKs Make Metaverse Content Creation Available to All appeared first on NVIDIA Blog.
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NVIDIA today announced NeuralVDB, which brings the power of AI to OpenVDB, the industry-standard library for simulating and rendering sparse volumetric data, such as water, fire, smoke and clouds. Building on the past decade’s development of OpenVDB, the introduction at SIGGRAPH of NeuralVDB is a game-changer for professionals working in areas like scientific computing and Read article >
The post Upping the Standard: NVIDIA Introduces NeuralVDB, Bringing AI and GPU Optimization to Award-Winning OpenVDB appeared first on NVIDIA Blog.
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Hailing from a small town in Italy, Matteo Bucci is determined to address some of the unknowns plaguing fundamental science.
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Understand ALL customer interactions with your brand even in a language that you don’t speak.
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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https://www.artificialconversation.com/p/breaking-metas-conversational-chatbot
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Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. By deploying a variety of edge IoT devices such as cameras, thermostats, and sensors, you can collect data, send it to the cloud, and build machine learning (ML) models to predict anomalies, failures, […]
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Saw this datascience interview question posted.
Let’s say you work at Google. You are developing a spam classifier to classify emails into spam vs. non-spam categories based on their content.
You try several different classifiers like SVM, Random Forests, etc., but none of them produce satisfactory results. So, you decide to combine them together by using stacking.
What classifier should you use as the meta-classifier in your stacking model and why?
So from my understanding, a meta classifier is essentially a model that takes as input feature the output from other models, and then provides a final prediction based on that. But what arguments are there for using a specific classifier as the top classifier?
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Machine learning is being used in almost every industry, including healthcare. However, due to the intrinsic complexity of healthcare data, classical machine learning faces various difficulties while dealing with these data. This is because healthcare outcomes like mortality, stroke, cancer initiation, and readmission frequently have a continuous time to events. Since time-to-event data frequently contains individuals whose outcomes are missing or censored owing to loss of follow-up, dealing with this type of data is much more difficult. The researchers have established that traditional classification and regression methods do not offer a simple solution to dealing with such clinical data.
Many researchers have been interested in applying deep neural networks, which may be used to create nonlinear representations of complex clin
A new study by Auton Lab at Carnegie Mellon University introduced the auton-survival package, a comprehensive Python library of user-friendly tools for machine learning applications in the presence of censored time-to-event data.
Continue reading | Check out the paper, package
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https://blenderbot.ai/
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https://www.kaggle.com/competitions/digital-turbine-auction-bid-price-prediction/overview
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Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator
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I'm using this https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Add_image_embeddings_to_LayoutLM.ipynb#scrollTo=i_IR1xhWMwty to prepares the data but it lacks of entities as output.
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Here's a link to the post.
As an aside, I'm Varun, CEO at Exafunction. We help companies do deep learning efficiently at scale. We're excited to start sharing the best practices for using GPUs that we've learned working at cutting edge deep learning companies in the past and with our current customers. Would love if you had any suggestions for deep dives we could do.
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We did an episode with Hod Lipson & Boyuan Chen on self-modeling of robot morphologies and sentient machines. I hope you may find it useful
Podcast Link:
Video: https://youtu.be/vR-5w7i2on8
Audio: https://soundcloud.com/ieeeras-softrobotics/hod-lipson-boyuan-chen-self-modeling-of-robot-morphologies?utm_source=clipboard&utm_medium=text&utm_campaign=social_sharing
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Here is the link to the repo https://github.com/Shreyz-max/Doodle-to-Image-Generator
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There are a few episodes of Attack on Titan that never came out in English. I've gotten about 30 minutes of isolated dialogue from each character and am praying someone knows of a TTS I can use.
tortoise-tts won't work because it uses it's own voices as a base so everyone comes out with a british accent.
And other entities with their TTS such as google or describe likely aren't going to let me use the copyrighted material.
If anyone knows something that can pull this off you'd be a godsend.
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One of the most fascinating things about virtual reality (VR) is the way it has evolved over time. You may have tried some basic 3D…
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Three techniques to process text: TFIDF, word2vector trained on our data, Gensim w2v — Natural language processing (NLP)
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3D content creators are clamoring for NVIDIA Instant NeRF, an inverse rendering tool that turns a set of static images into a realistic 3D scene. Since its debut earlier this year, tens of thousands of developers around the world have downloaded the source code and used it to render spectacular scenes, sharing eye-catching results on Read article >
The post NVIDIA Instant NeRF Wins Best Paper at SIGGRAPH, Inspires Creative Wave Amid Tens of Thousands of Downloads appeared first on NVIDIA Blog.
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Hello,
I would like to watch the talks/videos for accepted ICML 2022 papers. In the past, these used to be available for free at https://slideslive.com/library.
For example, the oral presentations (https://icml.cc/virtual/2022/events/oral) cannot be accessed without registration. However, with the conference being over, registrations are closed already.
Any ideas and tips on how to watch the videos would be very appreciated.
Thanks!
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Found a nice experiment on using sensor fusion and machine learning to detect smoke and get notified if the fire starts. Check this out: https://www.hackster.io/stefanblattmann/real-time-smoke-detection-with-ai-based-sensor-fusion-1086e6
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Innovative technologies in AI, virtual worlds and digital humans are shaping the future of design and content creation across every industry. Experience the latest advances from NVIDIA in all these areas at SIGGRAPH, the world’s largest gathering of computer graphics experts, running Aug. 8-11. At the conference, creators, developers, engineers, researchers and students will see Read article >
The post Dive Into AI, Avatars and the Metaverse With NVIDIA at SIGGRAPH appeared first on NVIDIA Blog.
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Pinterest has engineered a way to serve its photo-sharing community more of the images they love. The social-image service, with more than 400 million monthly active users, has trained bigger recommender models for improved accuracy at predicting people’s interests. Pinterest handles hundreds of millions of user requests an hour on any given day. And it Read article >
The post Pinterest Boosts Home Feed Engagement 16% With Switch to GPU Acceleration of Recommenders appeared first on NVIDIA Blog.
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It’s the first GFN Thursday of the month and you know the drill — GeForce NOW is bringing a big batch of games to the cloud. Get ready for 38 exciting titles like Saints Row and Rumbleverse arriving on the GeForce NOW library in August. Members can kick off the month streaming 13 new games Read article >
The post Rush Into August This GFN Thursday With 38 New Games on GeForce NOW appeared first on NVIDIA Blog.
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This is a guest post by Viktor Enrico Jeney, Senior Machine Learning Engineer at Adspert. Adspert is a Berlin-based ISV that developed a bid management tool designed to automatically optimize performance marketing and advertising campaigns. The company’s core principle is to automate maximization of profit of ecommerce advertising with the help of artificial intelligence. The […]
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submitted by /u/oodmb
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AI Weirdness: the strange side of machine learning
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If you can build a Machine Learning model — you should be able to deploy it
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https://reddit.com/link/wfl4nc/video/5ntmbzj9zkf91/player
https://reddit.com/link/wfl4nc/video/vul525t9zkf91/player
https://reddit.com/link/wfl4nc/video/13b738nbzkf91/player
Programming
Function from Description
Code to Explanation
Fix invalid Code
Translate Languages
Class from Description
Get Language from Code
Function from Docstring
Helpers
Regex from Description
Regex to Explanation
Linux Command
Get time complexity
Git Command from Description
Database
Text Description to SQL Command
Web
Generate HTML from Description
CSS from Description
Meta Tags from Description
I think this could be helpful to a lot of people (especially for beginner programmers). You can check out all functionalities on your own here:
programming-helper.com
Have fun using the tool ❤️
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Wondering about your workflow to train large models or run batch jobs that are either too big for you laptop? Do you use AWS VMs to run them and shut them back down after, SageMaker or AzureML?
I'm asking because I recently started working with https://github.com/dstackai/dstack which lets you run python jobs in AWS from your CLI but I'm not sure how others run their ML jobs.
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What are the Red Flags for Neural Suffering?
By [redacted] and [redacted]
Abstract:
Which kind of evidence would we need to see to believe that artificial neural networks can suffer? We review neuroscience literature, investigate behavioral arguments and propose high-level considerations that could shift our beliefs. Of these three approaches, we believe that high-level considerations, i.e. understanding under which circumstances suffering arises as an optimal training strategy, is the most promising. Our main finding, however, is that the understanding of artificial suffering is very limited and should likely get more attention.
- -
Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scien…
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Seeds of Science is a new journal (funded through Scott Alexander's ACX grants program) that publishes speculative or non-traditional articles on scientific topics. Peer review is conducted through community-based voting and commenting by a diverse network of reviewers (or "gardeners" as we call them).
We just sent out an article for review - "What are the Red Flags for Neural Network Suffering?" - that may be of interest to some in the r/MachineLearning, so I wanted to see if anyone would be interested in joining us a gardener to review the article. It is free to join and anyone is welcome (we currently have gardeners from all levels of academia and outside of it). Participation is entirely voluntary - we send you submitted articles and you can choose to vote/comment or abstain without …
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TL;DR: TorchDynamo (prototype from PyTorch team) plus nvfuser (from Nvidia) backend makes Bert (the tool is model agnostic) inference on PyTorch > 3X faster most of the time (it depends on input shape) by just adding a single line of code in Python script. The surprising thing is that during the benchmark, we have not seen any drawback implied by the use of this library, the acceleration just comes for free. On the same model, TensorRT is (of course) much faster, > 5X at least (and even more at batch size 1 which is impressive) but comes with its own complexity. The tool being a prototype, better performances are to be expected with more mature support of some backends, in particular regarding fx2trt (aka TensorRT mixed with PyTorch)!
Our TorchDynamo benchmark notebook can be found there:…
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I do not know if this is the correct subreddit to post this or not (if not please guide me) but I need a better voice annotation tool than this one (https://github.com/gong-io/gecko). Can anyone help?
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Amazon Comprehend is a natural-language processing (NLP) service you can use to automatically extract entities, key phrases, language, sentiments, and other insights from documents. For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface, or Amazon Comprehend APIs. In […]
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Amazon SageMaker Feature Store helps data scientists and machine learning (ML) engineers securely store, discover, and share curated data used in training and prediction workflows. Feature Store is a centralized store for features and associated metadata, allowing features to be easily discovered and reused by data scientist teams working on different projects or ML models. […]
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Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research
Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual tasks for multiple domains would need to build multiple models for each individual domain, train them independently (meaning no data is shared between domains), and then at inference time each model would process domain-specific input data. However, early layers between these models generate similar features, even for different domains, so it can be more efficient — decreasing latency and power consumption, lower memory overhead to store parameter…
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If you have 8 minutes to spare for my research project, follow the link below!
I'd like to hear your hypotheses about what leads people to see AI risk as important. I will test the most promising ones in a future poll. Many thanks!
https://docs.google.com/forms/d/e/1FAIpQLScT7M4_FssgBm6vvypNBW4gagzvESu5kJGP1j21CaU3N88rVw/viewform?usp=sf_link
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2 years ago, someone released an audio deepfake of Jordan Peterson reading absurdly vulgar rap lyrics. It was pretty amazing: video here
I want to learn how this was done and if any improvements to this process have been implemented since. What’s the easiest and most straightforward way to feed an algorithm hours of audio content of a person’s voice and synthesize an artificial replica of their voice that you can make say anything?
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https://morioh.com/p/afca6f2eec16
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Looking at all of the very large convolutional neural networks such as ResNets, VGGs, and the like, it begs the question on how we can make all of these networks smaller with less parameters while still maintaining the same level of accuracy or even improving generalization of the model using a smaller amount of parameters. […]
The post Using Depthwise Separable Convolutions in Tensorflow appeared first on Machine Learning Mastery.
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Bringing new AI and robotics applications and products to market, or supporting existing ones, can be challenging for developers and enterprises. The NVIDIA Jetson AGX Orin 32GB production module — available now — is here to help. Nearly three dozen technology providers in the NVIDIA Partner Network worldwide are offering commercially available products powered by Read article >
The post NVIDIA Jetson AGX Orin 32GB Production Modules Now Available; Partner Ecosystem Appliances and Servers Arrive appeared first on NVIDIA Blog.
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Autonomous vehicles are one of the most complex AI challenges of our time. For AVs to operate safely in the real world, the networks running within them must come together as an intricate symphony, which requires intensive training, testing and validation on massive amounts of data. Clément Farabet, vice president of AI infrastructure at NVIDIA, Read article >
The post Music to the Gears: NVIDIA’s Clément Farabet on Orchestrating AI Training for Autonomous Vehicles appeared first on NVIDIA Blog.
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Researchers use machine learning to automatically solve, explain, and generate university-level math problems at a human level.
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Meta AI released ViTDet - transformer based model for low shot object detection. It outperforms previous models on Large Vocabulary Instance Segmentation (LVIS) dataset.
Arxiv
Blog post
They have released code in their Detectron2 library.
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Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. Recently, AGI has been in the news with the Lambda sentient discussion We tend to think of AGI as a technical (algorithmic / data-driven) concept But the driver for AGI in our lives… Read More »The catalyst for AGI in our lives could be cultural rather than technical
The post The catalyst for AGI in our lives could be cultural rather than technical appeared first on Data Science Central.
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Banks and financial organizations continue to face myriad challenges in the market, such as data privacy concerns, accessibility to crucial banking data, and demand for better customer services, among many others. And it is increasingly recognized that the cloud is more than a technology; it enables banks and other financial services firms to store data… Read More »Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach
The post Banking and Financial Sector: Key Benefits of the Multi-Cloud Approach appeared first on Data Science Central.
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After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]
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New research ties inaccuracies in pulse oximeter readings to racial disparities in treatment and outcomes.
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Sponsored Post AI & Machine Learning now power most product experiences even beyond those of the big technology companies. Today, your models must perform and function correctly to ultimately deliver business value. The cost of deploying a slow or bad model, or not detecting undesirable behavior quickly, could significantly impact customer experience and the business’ […]
The post Mastering MLOps: Live Model Deployment & Inference Course with Stefan Krawczyk appeared first on Machine Learning Mastery.
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